A Combination of Machine Learning and Cerebellar Models for the Motor Control and Learning of a Modular Robot

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2017

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We scaled up a bio-inspired control architecture for the motor control and motor learning of a real modular robot. In our approach, the Locally Weighted Projection Regression algorithm (LWPR) and a cerebellar microcircuit coexist, forming a Unit Learning Machine. The LWPR optimizes the input space and learns the internal model of a single robot module to command the robot to follow a desired trajectory with its end-effector. The cerebellar microcircuit refines the LWPR output delivering corrective commands. We contrasted distinct cerebellar circuits including analytical models and spiking models implemented on the SpiNNaker platform, showing promising performance and robustness results
Original languageEnglish
Title of host publicationProceedings of the ICAROB International Conference on Artificial Life and Robotics 2017
Number of pages4
Publication date2017
StatePublished - 2017
Event2017 International Conference on Artificial Life and Robotics - Miyazaki, Japan

Conference

Conference2017 International Conference on Artificial Life and Robotics
LocationSeagaia Convention Center
CountryJapan
CityMiyazaki
Period19/01/201722/01/2017

    Keywords

  • Motor control, Cerebellum, Machine learning, Modular robot, Internal model, Adaptive behavior
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